Analysis of categorical breakdown by Carb and rec
library(foreign)
library(magrittr)
library(tidyverse)
library(ggmosaic)
library(janitor)
library(plotly)
myData <- read.dbf("TalamhPrescriptions_forVisualisation.dbf")
myData$rec_opt <- as.character(myData$rec_opt)
myData$rec_opt[is.na(myData$rec_opt)]="NONE"
myData$carb_opt[is.na(myData$carb_opt)]="NONE"
## Warning in `[<-.factor`(`*tmp*`, is.na(myData$carb_opt), value =
## structure(c(3L, : invalid factor level, NA generated
tabyl(myData$rec_opt)
## myData$rec_opt n percent
## CONV_TO_SNW 1665 0.013776042
## NONE 116424 0.963280436
## NOTHIN_MMAI 1646 0.013618838
## RETAIN 1127 0.009324684
myData.Report <- myData %>% group_by(rec_opt, carb_opt) %>% summarize( GIS_AREA = sum(gis_area) )
## `summarise()` has grouped output by 'rec_opt'. You can override using the `.groups` argument.
myData.Report <- myData.Report %>% mutate(OverLap = "No-Overlap")
myData.Report$OverLap[ as.character(myData.Report$rec_opt)== as.character(myData.Report$carb_opt)] =
as.character(myData.Report$rec_opt)[ as.character(myData.Report$rec_opt)== as.character(myData.Report$carb_opt)]
myData.Report$rec_opt <- as.character(myData.Report$rec_opt)
myData.Report$rec_opt[myData.Report$rec_opt %in% c("4YearGreenUp","4YearGreenUp_catch","standardmgmt")] = "NOTHIN_MMAI"
myData.Report$carb_opt <- factor(myData.Report$carb_opt,levels = c("NOTHIN_MMAI","CONV_TO_SNW","RETAIN","CONV_TO_SNW_MINERAL","REWET"))
myData.Report %>% tabyl(rec_opt)
## rec_opt n percent
## CONV_TO_SNW 5 0.2631579
## NONE 5 0.2631579
## NOTHIN_MMAI 5 0.2631579
## RETAIN 4 0.2105263
p <- myData.Report %>% mutate( rec_opt = as.character(rec_opt),
carb_opt = as.character(carb_opt)) %>%
ggplot() +
geom_mosaic(aes(weight=GIS_AREA, x = product(carb_opt), fill = rec_opt )) +
scale_fill_manual(values=c("#29ACB1","#285236","#8DBF5A","#1E5631","#19FCA1","#BC21A6")) +
theme( panel.background = element_rect(fill = "white",
colour = "white",
size = 0.5, linetype = "solid"),
panel.grid.major = element_line(size = 0.5, linetype = 'solid',
colour = "white"),
panel.grid.minor = element_line(size = 0.25, linetype = 'solid',
colour = "white"),
axis.text.x = element_text(angle = 45),
axis.text = element_text(face="bold"),
axis.title = element_text(size = 18),
plot.title = element_text(size = 24)) +
xlab("Carbon") + ylab("rec option") +
ggtitle("Project Talamh")
ggplotly(p,tooltip="text")
myData.Report %>% filter(OverLap=="No-Overlap") %>% mutate( rec_opt = as.character(rec_opt),
carb_opt = as.character(carb_opt)) %>% ggplot( aes(x=rec_opt, y=carb_opt)) +
geom_tile(aes(fill = GIS_AREA))+
scale_fill_distiller(palette = "YlGn") +
labs(title = "HeatMap",
y = "GIS AREA") + theme_bw()
ggplot(data=myData.Report, aes(x=OverLap, y=GIS_AREA, fill=OverLap)) +
geom_bar(stat="identity")
ggplot(data=myData.Report, aes(x=carb_opt, y=GIS_AREA, fill=rec_opt)) +
geom_bar(stat="identity")
myData.Report.2 <- myData.Report %>% filter(rec_opt !="NONE")
p <- myData.Report.2 %>% mutate( rec_opt = as.character(rec_opt),
carb_opt = as.character(carb_opt)) %>%
ggplot() +
geom_mosaic(aes(weight=GIS_AREA, x = product(carb_opt), fill = rec_opt )) +
scale_fill_manual(values=c("#29ACB1","#285236","#8DBF5A","#1E5631","#19FCA1","#BC21A6")) +
theme( panel.background = element_rect(fill = "white",
colour = "white",
size = 0.5, linetype = "solid"),
panel.grid.major = element_line(size = 0.5, linetype = 'solid',
colour = "white"),
panel.grid.minor = element_line(size = 0.25, linetype = 'solid',
colour = "white"),
axis.text.x = element_text(angle = 45),
axis.text = element_text(face="bold"),
axis.title = element_text(size = 18),
plot.title = element_text(size = 24)) +
xlab("Carbon") + ylab("rec option") +
ggtitle("Project Talamh")
ggplotly(p,tooltip="text")
myData.Report.2 %>% filter(OverLap=="No-Overlap") %>% mutate( rec_opt = as.character(rec_opt),
carb_opt = as.character(carb_opt)) %>% ggplot( aes(x=rec_opt, y=carb_opt)) +
geom_tile(aes(fill = GIS_AREA))+
scale_fill_distiller(palette = "YlGn") +
labs(title = "HeatMap",
y = "GIS AREA") + theme_bw()
ggplot(data=myData.Report.2, aes(x=OverLap, y=GIS_AREA, fill=OverLap)) +
geom_bar(stat="identity")
ggplot(data=myData.Report.2, aes(x=carb_opt, y=GIS_AREA, fill=rec_opt)) +
geom_bar(stat="identity")